Model tuning. We experiment with models from the well established MatchZoo [1]} library,https://github.com/NTMC-Community/MatchZoo which itself is based on Keras and TensorFlow. The MatchZoo library contains a tuning module that fine-tunes the models based on pre-defined model-specific hyper-parameter spaces [1]}. In the tuning process parameters are sampled from the hyper-space, but this sampling is not random, the scores of past samples will have an effect on the future selection process in a way that it yields a better score. The tuner uses Tree of Parzen Estimators (TPE) [3]} to search the hyper-space. We start tuning from the default parameter values provided by MatchZoo, and select hyper-parameters for all models based on a fixed validation set in 50 rounds.
